import sys
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()
results_folder = 'mmvec_major_taxa_scrambled'
results_base_name = 'latent_dim_3_input_prior_1.00_output_prior_1.00_beta1_0.90_beta2_0.95'
table = pd.read_table(results_folder + '/' + results_base_name + '_ranks.txt', index_col=0)
table.head()
| Propionibacteriaceae | Staphylococcus caprae or capitis | Staphylococcus epidermidis | Staphylococcus hominis | Other Staphylococci | Polyomavirus HPyV6 | Polyomavirus HPyV7 | Merkel Cell Polyomavirus | Malasseziaceae | Corynebacteriaceae | Micrococcaceae | Other families | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| featureid | ||||||||||||
| X940001 | 0.108002 | 0.281476 | 0.230751 | -0.088645 | 0.325523 | 0.159858 | 0.003797 | 0.040958 | 0.541223 | 0.191130 | 0.089953 | 0.273742 |
| X940002 | -0.311842 | -0.030911 | 0.145908 | -0.223350 | 0.145753 | 0.025062 | -0.045640 | 0.148346 | -0.102332 | 0.175697 | -0.079407 | 0.127062 |
| X940005 | -0.144484 | -0.046149 | -0.087393 | -0.239615 | -0.052742 | -0.087412 | -0.162386 | -0.153883 | 0.000429 | -0.073136 | -0.126851 | 0.190019 |
| X940007 | 0.219706 | 0.499016 | 0.520032 | 0.120489 | 0.626079 | 0.455443 | 0.365660 | 0.403363 | 0.544929 | 0.532255 | 0.324215 | 0.925777 |
| X940010 | 0.497619 | 0.113296 | 0.059617 | 0.605515 | -0.038072 | 0.152955 | 0.305821 | 0.190868 | 0.073865 | 0.026769 | 0.315798 | -0.430855 |
table['Selected'] = np.isin(table.index,
['X940203', 'X940589', 'X940625', 'X940925', 'X940936', 'X942191',
'X942237', 'X950023', 'X950028', 'X950056', 'X950157', 'X950173',
'X950193', 'X950225', 'X950228', 'X950233', 'X950254', 'X950396',
'X950485', 'X950584', 'X950661', 'X950999', 'X960035', 'X960242',
'X960306', 'X960421', 'X960463', 'X960465', 'X960712', 'X960726',
'X960934', 'X961553', 'X961686', 'X970018', 'X970091', 'X970092',
'X970232', 'X970283', 'X970327', 'X970342', 'X970633', 'X970680']
)
table.sort_values('Selected', inplace=True)
sns.relplot(
table,
y='Propionibacteriaceae', x='Staphylococcus epidermidis', hue='Selected'
)
<seaborn.axisgrid.FacetGrid at 0x7fa01ea0b410>
sns.pairplot(table, hue='Selected')
<seaborn.axisgrid.PairGrid at 0x7fa01ea2c810>
for i in table.columns[:-1]:
sns.displot(table, x=i, hue='Selected', multiple='stack')